Habit Machine: AI Product Management

Vladimir Dyachkov PhD
  • 0.0 (0)
  • Management
  • Updated Weekly

AI changes everything. But human nature stays the same. Learn to build products that respect attention, reduce friction, and earn repetition. AI has turned product management upside down. Static interfaces are dying. Users now expect products that anticipate, adapt, and execute without asking. The old playbook — roadmaps, backlogs, stakeholder alignment — still exists. It's just no longer enough to win. This book is for product leaders who feel the shift. The author spent 20 years building at scale — AI products, apps for 180 million users. And he holds a PhD in behavioral economics.

  1. 6d ago

    The Friction Tax — Why Every Extra Click Is a Confession of Laziness | Habit Machine Podcast

    Episode 23: The Friction Tax — Why Every Extra Click Is a Confession of Laziness | Habit Machine Podcast How Feature Bloat, Captchas, and "Are You Sure?" Dialogs Are Stealing Your Users' Trust — and the 4-Step Audit to Restore Invisible Simplicity Episode Overview You survived the scaling chaos. But something else crept in—the product feels heavy. Menus everywhere. Options nobody uses. Friction is never a necessary evil; it is always a design failure. Two Product Managers dismantle the cognitive tax we pass to users because we didn't solve problems invisibly. Security is the team's obligation, never the user's—passkeys, magic links, and silent risk checks absorb complexity behind the scenes. The conversation exposes seven patterns of justified friction that are actually laziness: registration before value, configuration overload, interruptive monetization, opaque data collection, latency and decorative delays, confirmation overload, and homework onboarding. It then reveals the three illusions that keep us adding weight—"users asked for it," measuring shipping volume, and competitor panic—and offers four strategies to protect coherence: remove relentlessly, hide complexity until proven necessary, measure complexity as a metric, and build teams that are allowed to simplify. The episode closes with a quick subtraction audit to separate products that protect the simplicity edge from those paying the bloat penalty. Simplicity is not a feature. It is the discipline of absorbing complexity so the user never has to. What You Will Learn Why every captcha, verification wall, and confirmation dialog is a tax on attention—and how to make security invisibleThe seven patterns of "justified" friction that are actually design failures: registration before value, configuration overload, interruptive monetization, opaque data collection, latency and decorative delays, confirmation overload, and homework onboarding Key Takeaways "Simplicity is not a feature. It is the discipline of absorbing complexity so the user never has to. Every extra step, even a well‑intended one, multiplies interaction cost. The core job gets buried under our internal needs. Remove relentlessly. Hide until proven necessary. Measure complexity in every sprint. And build teams that are allowed to simplify—because courage to remove is harder than the ease to add." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproductEmail: vladimiruso@gmail.comTelegram: t.me/vlrusoReady to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and replace growth hope with distribution architecture. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, virality engineering, and removing the friction that kills habit. Your browser does not support the audio element. Episode 23 preview — full episode available now on all podcast platforms.

  2. Jul 21

    Growth Is a Trap — The 5 Ways Scaling Destroys Your Product | Habit Machine Podcast

    Episode 22: Growth Is a Trap — The 5 Ways Scaling Destroys Your Product | Habit Machine Podcast Why Surviving the Chaotic Middle Is the Only Test That Proves Your Success Was Real, and How to Scale Without Burning Everything Down Episode Overview You found product-market fit. Users are flooding in. The team is euphoric. This episode is your cold shower. Growth is not a victory lap—it is a brutal stress test that exposes every fragile assumption and skipped process from the early days. Two Product Managers dissect the four predictable phases of product evolution and reveal why misreading your stage is how teams optimize for the wrong metrics and burn runway. The conversation moves from the search for the core job to active growth chaos, maturity optimization, and the stagnation nobody wants to admit. It then exposes the five killers that strike during the scaling phase: infrastructure cracking under load, retention decaying while acquisition rises, support collapsing under volume, core value dilution through feature bloat, and community quality degradation. The episode closes with a survival framework—clear ownership boundaries, documented decision frameworks, strict feature acceptance criteria, and the hard rule: if any critical metric dips below three, pause growth and fix the systems first. Complexity does not disappear when you ignore it. It compounds silently until it breaks everything. What You Will Learn Why growth is not a victory lap—it's the test that reveals whether your success was real in the first placeThe four predictable phases: product-market fit, active growth, maturity, and stagnation/decline—and why misreading your stage kills runwayThe critical retention threshold: Day 30 stabilization above 40% before you even think about scaling reachThe five killers of active growth: infrastructure cracks, retention decay, support collapse, core value dilution, and community degradationWhy novelty attracts but habit retains—and how to build repeat-use triggers from day one, not bolt them on after the leak startsHow to deploy retrieval-augmented assistants to protect human agents from repetitive queries and keep support a frontline retention engineWhy more surface area means more cognitive load—and how to reject features that do not strengthen the core behaviorThe hard rule: pause growth if any critical metric dips below three—fix the systems first before scaling furtherWhy chaos was a feature at five people but a liability at fifty—and how to preserve speed through clarity, not hallway conversationsKey Takeaways "Scaling is not what happens after success. It is the test that reveals whether the success was real in the first place. Complexity does not disappear when you ignore it. It compounds silently until it breaks everything. If retention dips while acquisition climbs, you are buying attention, not building habit. Pause growth. Fix the systems. Then scale." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproductEmail: vladimiruso@gmail.comTelegram: t.me/vlrusoReady to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and replace growth hope with distribution architecture. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, virality engineering, and removing the friction that kills habit. Your browser does not support the audio element.Episode 22 preview — full episode available now on all podcast platforms.

  3. Jul 14

    The Normality Illusion & Institutional Lock-In | Habit Machine Podcast

    Episode 21: The Normality Illusion & Institutional Lock-In | Habit Machine Podcast Why Growth Without Pattern Stabilization Is Just Expensive Noise, and How to Engineer Behavioral Normality Before It's Too Late Episode Overview Downloads climb. Daily active users look healthy. Most teams declare victory and scale. This episode dismantles that trap. Normality is not a finish line—it's when the behavior reproduces itself without you pushing it. Two Product Managers dissect why retention without pattern specificity is a vanity metric, and why institutional analysis asks a fundamentally different question: what pattern of behavior emerged from your signal, how stable is it across contexts, and how does it interact with other routines in a user's life? The conversation moves from surface metrics to the five real signals of normality—active user intensity, frequent usage cadence, ongoing economic behavior, organic spread, and pattern stability. It then exposes the false signals that trick teams: likes, views, downloads, and hype that fades fast. The episode closes with a five-point diagnostic that separates products that have achieved behavioral lock-in from those pouring users into a leaky bucket. Normality is not permanent. Once a pattern becomes routine, the challenge shifts from formation to defense. Competitors send counter signals. The environment changes. Your product succeeds not by becoming permanent, but by remaining adaptive within a changing informational environment. What You Will Learn Why growth without pattern stabilization is just expensive noise—and how to distinguish exposure from adoptionThe five real signals of normality: high active user intensity, frequent usage cadence, ongoing economic behavior, organic spread, and pattern stability across contextsThe false signals that trick teams: likes, views, downloads, and hype that fades fastHow institutional analysis replaces traditional marketing questions—rewiring daily rhythms instead of optimizing for clicksWhy Day 7 and Day 30 retention are useful quick signals but don't tell you why users return or what alternative patterns they are rejectingThe five-point diagnostic: Is Day 7 retention stabilizing above 40% for your core cohort? Does LTV exceed CAC by at least 3:1? Is organic referral driving a meaningful share of new activations? Have you mapped unit economics per behavioral segment? Can you prove that a majority of retained users complete the core job to be done at least weekly?Why normality is not a finish line—the challenge shifts from formation to defense, and your product must remain adaptive within a changing informational environmentKey Takeaways "Habits compound. Hype decays. Build for the former. Normality is not a finish line—once a pattern becomes routine, the challenge shifts from formation to defense. Competitors send counter signals. The environment changes. Your product succeeds not by becoming permanent, but by remaining adaptive." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproductEmail: vladimiruso@gmail.comTelegram: t.me/vlrusoReady to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and replace growth hope with distribution architecture. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, virality engineering, and removing the friction that kills habit.

  4. Jul 7

    The Hidden "Friction Tax" That Kills 90% of Habits Before They Start

    Episode 21: The Next One | Habit Machine Podcast Why Normality Is Engineered, Not Hoped For, and How to Know When Your Product Has Actually Become a Habit Episode Overview Downloads climb. Daily active users look healthy. But is that growth real, or just expensive noise? This episode kills the myth that retention metrics tell the full story and reveals the institutional framework that separates products that fade from those that become normal. The conversation begins where virality ends—pattern stabilization. Five signals separate genuine behavioral lock-in from vanity metrics: high active user intensity, frequent usage cadence, ongoing economic behavior, organic spread, and pattern stability across contexts. The episode then dismantles the false signals that trick teams—likes, views, downloads—and provides a five-point diagnostic that cuts through the noise. The episode closes with a truth: normality is not a finish line. Once a pattern becomes routine, the challenge shifts from formation to defense. Competitors send counter signals. The environment changes. Your product succeeds not by becoming permanent, but by remaining adaptive within a changing informational environment. What You Will Learn The five signals of normality: high active user intensity, frequent usage cadence, ongoing economic behavior, organic spread, and pattern stability across contextsWhy Day Seven and Day Thirty retention are useful quick signals but do not tell you why users return or what alternative patterns they are rejectingThe false signals that trick teams: likes, views, downloads—they measure exposure, not adoptionHow institutional analysis asks different questions: what pattern of behavior emerged from your signal? How stable is that pattern across different contexts? How does it interact with other routines in a user's life?The five-point diagnostic: Day Seven retention stabilizing above forty percent for your core cohort, LTV exceeding CAC by at least three to one, organic referral driving a meaningful share of new activations, unit economics mapped per behavioral segment, and proof that a majority of retained users complete the core job to be done at least weeklyWhy scoring below three on the diagnostic means you are optimizing for surface metrics instead of behavioral lock-inThe core principle: normality is not a finish line—once a pattern becomes routine, the challenge shifts from formation to defenseKey Takeaways "Growth without pattern stabilization is just expensive noise. Habits compound. Hype decays. Build for the former. Normality is not a finish line—once a pattern becomes routine, the challenge shifts from formation to defense. Your product succeeds not by becoming permanent, but by remaining adaptive within a changing informational environment." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproductEmail: vladimiruso@gmail.comTelegram: t.me/vlrusoReady to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and replace growth hope with distribution architecture. Habit Machine AI Product Management https://www.amazon.com/Habit-Machine-AI-Product-Management-ebook/dp/B0GYYP119X Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, virality engineering, and removing the friction that kills habit.

  5. Jun 30

    How Products Become Invisible Infrastructure That Society Can’t Unthink

    Episode 18: The Institutional Layer | Habit Machine Podcast Episode 18: The Institutional Layer | Habit Machine Podcast How Products Become Invisible Infrastructure That Society Can’t Unthink Episode Overview The highest success is not being a tool users choose—it is becoming the environment they operate within without a second thought. In this episode, two Product Managers dissect the institutional layer: the sequence that turns a novel signal into a social default, why the same signal can spawn unintended patterns, and how to map the spectrum of behavioral responses instead of just the target. The conversation redefines the product manager as an institutional engineer who measures pattern formation, not feature adoption, and reveals the four traps that turn a promising signal into a costly institutional failure. The ultimate moat is not code; it is making your solution feel so inevitable that switching away feels like breaking gravity. What You Will Learn The five-stage institutional sequence: signal introduction, variation, reinforcement, routine stabilization, and normative force Why you can design signals but never fully control the interpretations—and how cultural identity can hijack a purely functional bet Institutional cartography: measuring the full spectrum of behavioral clusters, not just the intended response, to see which patterns are displacing which The four traps: optimizing only for the target, confusing correlation with causation, treating institutional change as one-off, and ignoring competing legacy patterns How to make a product the path of least cognitive resistance so that staying becomes the default and leaving feels irrational Key Takeaways "Products that become norms do not just offer a better solution. They reduce cognitive load below the threshold of alternatives. The moat that lasts is not code—it is habit, pattern maintenance, and making your solution feel so inevitable that switching away feels like breaking gravity." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproduct Email: vladimiruso@gmail.com Telegram: t.me/vlruso Ready to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and learn to build the institutional layer that outlasts every feature war. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, institutional cartography, and the patterns that turn products into the environment.

  6. Jun 23

    Why Relevance Beats Innovation, and How to Map Your Product Signal to the Actual Human Need

    Episode 17: Need-Signal Alignment | Habit Machine Podcast Why Relevance Beats Innovation, and How to Map Your Product Signal to the Actual Human Need Episode Overview A sharp signal that misses the real human motivation is just noise. This episode builds on the behavioral proposition of a launch by aligning it with the hierarchy of needs that actually drives user behavior—from urgent physiological relief to long-term meaning. Two Product Managers climb the pyramid layer by layer, showing why the most powerful signals reduce explanation to instinct. The conversation delivers five concrete rules for need-signal alignment and a litmus test: if your message doesn't resonate in a low-fidelity prototype, it will never scale. What You Will Learn How to map your product to the exact motivational layer—from immediate cognitive relief to aspirational growth—and why the depth of the need determines how much persuasion you requireWhy physiological and safety needs demand signals shorter than hesitation, while social and esteem needs require visible validation loops and a focused homeThe aspiration trap: making deferred goals feel immediate by replacing vague promises like “unlock your potential” with concrete, near-term milestonesThe five alignment rules: define the need precisely, make value legible in under three seconds, deliver in the right context, strip cognitive load from the message, and test message-need fit with AI prototypes before writing codeHow to validate resonance using vibe-coded mockups and AI segmentation—and why conversion at the signal stage is the only real proof of alignmentAbout the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproductEmail: vladimiruso@gmail.comTelegram: t.me/vlrusoReady to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and align your signal with the need that converts curiosity into habit. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, signal engineering, and the needs that make products inevitable.

  7. Jun 16

    The Information Signal: How a Product Rewires Behavior

    Episode 16: The Signal That Rewires Habits | Habit Machine Podcast Episode 16: The Signal That Rewires Habits | Habit Machine Podcast Why a Launch Is a Behavioral Proposition, Not a Marketing Campaign Episode Overview Most products don't fail because engineering was slow—they fail because the signal never lands. In this episode, two Product Managers redefine the relationship between product and market. A launch is not a press release or a burst of ads. It is an information signal that must rewire a routine by promising less work, fewer decisions, and instant cognitive relief. We map the three paths a product can take—capturing the default, fading into noise, or mutating into an unexpected institution—and break down the three psychological thresholds a signal must pass to even begin the journey. The episode closes by distinguishing a slogan that sells a feature from a signal that sells a new behavioral contract, and teases the next critical layer: Need-Signal Alignment. What You Will Learn Why a launch is a behavioral proposition that promises a less frustrating way to do the job The three market paths: capturing the default, fading into noise, and mutating into an unexpected institution The three psychological thresholds for a strong signal—cognitive fluency, friction reduction, and contextual timing Why a signal must be graspable in under three seconds and promise relief, not just power How to write a behavioral contract that focuses on what users stop doing, not what they start doing The difference between sounding innovative and sounding inevitable, and why that distinction determines adoption Key Takeaways "A slogan sells a feature. A signal sells a new routine. When your positioning focuses on what users stop doing instead of what they start doing, adoption accelerates. The goal is not to sound innovative—it is to sound inevitable." Coming Next Episode: Need-Signal Alignment—why curiosity must become habit, and how to map your value proposition to actual human motivation. About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproduct Email: vladimiruso@gmail.com Telegram: t.me/vlruso Ready to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and learn to send signals that become defaults, not noise. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, market signals, and the systems that turn curiosity into habit.

  8. Jun 9

    Behavioral Intelligence: The Art of Customer Research

    Episode 15: The Research That Ships | Habit Machine Podcast Episode 15: The Research That Ships | Habit Machine Podcast Why Users Can’t Tell You What to Build, and How Jobs to Be Done, Behavioral Personas, and Hybrid Journey Maps Reveal What They Actually Need Episode Overview Asking users what they want is the fastest route to building features nobody needs. This episode dismantles the polite fiction of feature-request research and replaces it with a rigorous, behavioral discipline. Two Product Managers walk through Jobs to Be Done that account for AI-era autonomy, personas grounded in cognitive load rather than demographics, journey maps that track emotional peaks and AI trust thresholds, and pain-and-gain analysis that connects retrieval quality directly to user anxiety. The output is not a research report—it is a testable hypothesis and a vibe-coded prototype within days. What You Will Learn How to ask “walk me through the last time” instead of “would you use this” to surface real workarounds and hidden motivation Writing one-sentence job statements that capture context, motivation, and outcome—and detecting whether the user is actually hiring an autonomous agent instead Building real personas from observed friction, decision triggers, and psychographic markers rather than fictional demographics Mapping the hybrid customer journey: emotional peaks, the Peak-End Rule, and where an AI-to-human handoff is mandatory to prevent churn Pain and Gain Analysis: categorizing friction that can be eliminated via retrieval-grounded outputs, and why stale AI results increase anxiety instead of providing relief Compressing research into action: using AI clustering and behavioral telemetry to validate the gap between what users say and do, translating findings directly into a concierge test or vibe-coded prototype Key Takeaways "Research is not a phase you complete before development. It is a continuous loop that informs every sprint. If your research hasn’t produced a clear behavioral hypothesis and a testable prototype, you haven’t finished the job. You’ve just gathered opinions. And the market pays for outcomes, not opinions." About the Book Title: Habit Machine: AI Product Management Series: AI and Human, Volume 1 Author: Vladimir Dyachkov, PhD ISBN: 978-83-8455-089-2 Habit Machine is a practical playbook for Product Managers, founders, and builders who engineer products that change behavior, not just ship features. About the Author Vladimir Dyachkov, PhD is a Product leader in AI with a PhD in Economics and two decades of experience building products people actually use. Connect with Vladimir Dyachkov LinkedIn: linkedin.com/in/uxproduct Email: vladimiruso@gmail.com Telegram: t.me/vlruso Ready to Engineer Habits, Not Just Features? Grab your copy of Habit Machine: AI Product Management and turn user research into a prototype, not a report. ISBN: 978-83-8455-089-2 Part of the AI and Human series. Subscribe to the Habit Machine Podcast for more on Behavioral Design, Jobs to Be Done, and the research that actually ships.

See All (21)

About

AI changes everything. But human nature stays the same. Learn to build products that respect attention, reduce friction, and earn repetition. AI has turned product management upside down. Static interfaces are dying. Users now expect products that anticipate, adapt, and execute without asking. The old playbook — roadmaps, backlogs, stakeholder alignment — still exists. It's just no longer enough to win. This book is for product leaders who feel the shift. The author spent 20 years building at scale — AI products, apps for 180 million users. And he holds a PhD in behavioral economics.

Information